Pilots in aircraft on attack runs are overloaded with information, with enemies attacking, and on a short timescale. One difficulty they have is that when looking down at their displays with an aimpoint, it is not always immediately obvious what the scale is, nor what the relative effects of their munitions will be.
Another common problem is in mission planning. Graphical representations of predicted weapon system effectiveness can greatly speed up the process of mission planning, selecting optimal ordinance to use in a given sortie, and estimating probability of success of a mission before committing forces or other resources.
Predicted weapon effectiveness is calculated using Joint Munition Effectiveness Manuals (JMEMs). Unfortunately, JMEM data and algorithms only yield numerical output and there are so many possible variations of weapon systems and initial conditions that no one can “memorize” or otherwise maintain an intuitive yet accurate feel for it. What is needed is a way to graphically represent JMEM data in a human understandable form. Such a graphical representation would not only speed up the process of mission planning, but would also allow the warfighter to make mission and weapon selection adjustments in the field to correct for unexpected errors in intelligence data or enemy force placement.
Overview of the Joint Munition Effectiveness Manuals (JMEMs)
The Joint Technical Coordinating Group for Munitions Effectiveness (JTCG/ME) publishes Joint Munition Effectiveness Manuals, containing information about the effectiveness of various weapons against various targets, under various weapon release conditions. JMEM 61A1-1-1-1 combines statistical theory and tables of live-fire test data into algorithms to determine, among other things, the probability that a particular air-to-ground weapon, launched under particular conditions, will destroy a particular target.
The basic algorithm can be derived as follows. A Gaussian probability distribution describes the location at which a weapon will land, as a function of distance from the weapon aimpoint. The variance of the Gaussian depends on a number of factors, such as the weapon guidance method (e.g., unguided, laser guided, GPS guided) and the angle between weapon trajectory and ground plane at impact. These factors provide different variance in range and deflection directions (parallel and perpendicular to weapon trajectory, respectively), resulting in a two-dimensional, elliptical probability distribution. A scaled Gaussian distribution, or a scaled step function, describes the probability that a weapon will destroy a target at a given distance from its impact location. These distributions depend on factors such as the strength of the warhead and the type of target, and may be different in range and deflection directions. The convolution of the two distributions provides a single distribution describing the probability of destroying a target (PKILL) as a function of distance from weapon aim point, weapon type, target type, and release conditions. JMEM algorithms calculate and report PKILL at the weapon aimpoint.
The JMEM algorithms feature more advanced capabilities as well. They calculate the effectiveness against area targets by integrating the portion of the PKILL distribution that overlaps an area. They calculate the effectiveness of multiple weapons against a single target using standard statistical combination (pn=1−(1−p)n). They calculate the effect of a series of potentially overlapping weapon impacts as the integrated probability of kill of each weapon, multiplied by the number of weapons, divided by the area covered by the series of impacts. They calculate PKILL for cluster weapons, using the above calculations for area targets and series of weapons to generate a cluster weapon's damage distribution within its blast pattern, approximate that as a step function, and apply the basic algorithm.